The regulation of IRF-4 activity in lymphoid cells and involvement in HTLV-I-induced T cell leukomogenesis /
Bibliographic record
Abstract
L'auteur a accord une licence non exclusive permettant la Bibliothque nationale du Canada de reproduire, prter, distribuer ou vendre des copies de cette thse sous la forme de microfiche/film, de reproduction sur papier ou sur format lectronique.L'auteur conserve la proprit du droit d'auteur qui protge cette thse.Ni la thse ni des extraits substantiels de celle-ci ne doivent tre imprims ou autrement reproduits sans son autorisation.0-612-78724-9 Canada To my parents, Daniel and Annie for their encouragement, love and support. mes parents Daniel et Annie pour leurs encouragements, amour et support.To my sisters, Eve and Myriam for their support, sense of humor and of course of style. mes soeurs Eve et Myriam pour leur support, leur sens de l'humour et bien sr de style.To Anna for ail her support and cheering up.A Anna pour son support et ses encouragements.IV PP2A.,These interactions could be involved in HTLV-I induced leukemogenesis.Novel IRF-4 regulated genes were also analyzed using an IRF-4 stably expressing Jurkat cell line and cDNA array technology.Several genes potentially regulated by IRF-4 such as RhoA, HSC70, RP-A, cyclin BI, PCNA, EBI, NIP3, MAPKK3 were identified.The deregulation of these genes by IRF-4 could lead to an increase in cellular proliferation and activation, a decrease in apoptosis and in DNA repair; Events that are hallmarks of HTLV-l, induced T cellieukemogenesis.v want to acknowledge your attempts to make me calI you by your first name.Maybe now that l have a Ph.D. l might just do so (probably not!).Thank you for making me shorten my already huge thesis!l wish you of course continued success and happiness.May the years to come bring you many conferences in warm and exotic places.Thank you Dr. Lin for aIl your technical help and good advice.l can only hope one day to be as efficient as you.l will always remember waiting in line at the Louvre in Paris and how it only took us two hours to see "everything" in this museum.l
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".